Description:
This is a doctoral course on statistical models suitable for Kellogg PhD students as well as PhD students from related fields such as statistics, economics, and engineering. The course is taught in the spring and topics alternate from year to year. Currently, in odd years the course is on Bayesian methods and computation and covers simple parametric models, regression models, hierarchical models, mixture models, optimization algorithms, Monte Carlo simulation algorithms, model checking, nonparametric models, and hidden Markov models while in even years the course is on applied and computational statistics and covers statistical graphics and exploratory data analysis, permutation tests, null tests, the bootstrap, smoothing, cross-validation, tree-based and linear regression, model selection, bagging, principal components analysis, and cluster analysis. Marketing applications include but are not limited to conjoint analysis, choice models, data minimization, perceptual maps, etc.